Clinical BenchmarkThe short answer
Accuracy is a count divided by a denominator. Understanding those two quantities is necessary, but it is not enough to interpret a model comparison. This guide separates three questions that often get mixed together: how much one question changes the score, how a binomial interval behaves, and what changed in the evaluated system between two runs.
Calculate resolution before adding decimal places
The MedQA USMLE test partition contains 1,273 questions. One extra correct answer therefore changes accuracy by approximately 0.0786 percentage points. This is a direct arithmetic property of the denominator. Reporting many more decimal places does not create finer underlying answer-selection evidence.
Resolution is not uncertainty. It tells you the smallest step produced by one changed binary outcome, not how well the observed questions represent another set. A result table can include both the correct count and percentage so the reader can see what the rounded number represents without reverse-engineering it.
Use the interval for the question it answers
Our calculator implements the Wilson score interval described by NIST. Its default success count is deliberately hypothetical; it is not an evaluation of a named model. Changing the count or denominator shows how a descriptive binomial interval responds to the amount and balance of observed evidence.
That illustration assumes independent binary outcomes under an appropriate sampling model. It does not measure uncertainty from benchmark contamination, repeated question patterns, subjective references or changes in the target population. An attractive narrow interval cannot compensate for those limitations because they are outside the calculation’s inputs.
Keep paired comparisons separate
Two models evaluated on the same questions produce paired outcomes. They may make many of the same errors, or disagree on different cases while obtaining similar totals. Two standalone accuracy intervals discard that pairing and therefore cannot answer every question about the difference between the systems.
Preserve question-level predictions when designing a comparison. A statistical analysis can then use the structure of the actual experiment. Do not treat overlapping intervals as a universal proof of no difference or nonoverlap as a substitute for reviewing the study design. The calculator is an educational view of one proportion.
Read the retrieval experiment as a pipeline experiment
The original MedMCQA paper reports PubMedBERT test accuracy of 41 with no context, 42 with Wikipedia context and 47 with PubMed context, expressed here on a 100-point accuracy scale. These are historical results under the original fine-tuning protocol, not estimates of present-day frontier capability.
Their analytical value is the named change in context condition. The six-point gap between no context and PubMed belongs to the evaluated pipeline. It should not be retold as the effect of replacing the base model. Retrieval quality, available evidence and reader behavior are all relevant to explaining such a comparison.
Choose a conclusion that preserves both facts
A good report attaches uncertainty to the outcome it actually models and attaches system changes to the configuration that actually changed. Keep these statements separate: the observed count, its chosen interval, and the comparison conditions. That structure makes it harder for a single headline percentage to conceal several different claims.
When extending either benchmark, publish a compact experiment receipt and retain raw predictions. If the purpose is clinical deployment, plan additional evidence around the workflow and consequences of use. Examination accuracy can help prioritize that work, but an interval around an exam score does not turn it into an estimate of patient benefit.
References & further reading
These original sources support the methods discussed here. Our suggested planning steps are editorial guidance, not an endorsement by the source authors.
- MedQA: What Disease Does This Patient Have? ↗Di Jin and colleagues. Original MedQA definitions, split statistics and historical retrieval baselines.
- MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset ↗Ankit Pal, Logesh Kumar Umapathi and Malaikannan Sankarasubbu. Original exam-based construction, exact split statistics and retrieval comparison.
- Confidence intervals for a proportion ↗NIST/SEMATECH. Wilson score interval formula; used only for the illustrative calculator.
Published by Arcophos. Educational material, not clinical advice or a claim of regulatory compliance. Read our editorial method.